Added pajama data generation

main.py

@@ -497,7 +497,7 @@ def update_outs_parallel(
     parser.add_argument(
         "dataset",
         type=str,
-        help="Dataset name [c4, pajama, refinedweb] or path to data where to extract calibration data from.",
+        help="Dataset name [c4, pajama] or path to data where to extract calibration data from.",
     )
     parser.add_argument(
         "--new_eval",

src/datautils.py

@@ -7,6 +7,7 @@
 import torch
 from datasets import load_dataset
 from packaging import version
+from tqdm import trange
 from transformers import AutoTokenizer, LlamaTokenizer
 
 
@@ -16,6 +17,26 @@ def set_seed(seed: Optional[int]):
     torch.random.manual_seed(seed)
 
 
+def get_red_pajama(nsamples, seqlen, tokenizer):
+    print("Loading red_pajama from togethercomputer/RedPajama-Data-1T-Sample")
+    traindata = load_dataset("togethercomputer/RedPajama-Data-1T-Sample", split="train")
+    tokenizer.bos_token_id = 1
+    tokenizer.eos_token_id = 2
+    trainloader = []
+    for _ in trange(nsamples, desc="Making red_pajama calibration set", leave=False):
+        while True:
+            i = random.randint(0, len(traindata) - 1)
+            trainenc = tokenizer(traindata[i]["text"], return_tensors="pt")
+            if trainenc.input_ids.shape[1] > seqlen:
+                break
+        i = random.randint(0, trainenc.input_ids.shape[1] - seqlen - 1)
+        j = i + seqlen
+        inp = trainenc.input_ids[:, i:j]
+        assert inp.shape[1] == seqlen
+        trainloader.append(inp)
+    return trainloader
+
+
 def get_wikitext2(nsamples, seqlen, tokenizer, eval_mode=False):
     if not eval_mode:
         traindata = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
@@ -165,9 +186,8 @@ def get_loaders(name, nsamples=128, seed=0, seqlen=2048, eval_mode=False, model_
     Loads and prepares data for a Transformers model.
     Args:
         name (str): The name of the dataset to load.
-        This can be one of 'wikitext2', 'c4', 'ptb' for datasets loaded from Huggingface datasets,
-        'pajama' or 'refinedweb' for pre-tokenized datasets in folder `data` or 'none' for cases
-        where a dataset is not needed, like RTN. It can also accept data path to custom file.
+        This can be one of 'wikitext2', 'c4', 'ptb','pajama' for datasets loaded from Huggingface datasets,
+        or 'none' for cases where a dataset is not needed, like RTN. It can also accept data path to custom file.
         nsamples (int, optional): The number of samples to load from the dataset. Defaults to 128.
         seed (int, optional): The random seed value for data shuffling and splitting. Defaults to 0.
         seqlen (int, optional): The maximum sequence length for input tokenization. Defaults to 2048.
@@ -186,11 +206,8 @@ def get_loaders(name, nsamples=128, seed=0, seqlen=2048, eval_mode=False, model_
     set_seed(seed)
 
     # for pre-tokenized datasets
-    if name.lower() == "pajama":
-        data = torch.load(f"./data/red_pajama_n=1024_{seqlen}_context_length.pth")[:nsamples]
-    elif name.lower() == "refinedweb":
-        data = torch.load("./data/refined_web_n=128.pth")[:nsamples]
-    elif name.lower() == "none":
+
+    if name.lower() == "none":
         print("Not loading any dataset. (OK if you use no compression or methods like RTN.)")
         return None
     elif os.path.isfile(name):
@@ -199,7 +216,7 @@ def get_loaders(name, nsamples=128, seed=0, seqlen=2048, eval_mode=False, model_
         except FileNotFoundError:
             raise FileNotFoundError(
                 f"Failed to load custom data from {name}.",
-                "Check data path or use one of [c4, wikitext2, ptb, pajama, refinedweb, none]",
+                "Check data path or use one of [c4, wikitext2, ptb, pajama, none]",
             )
     else:
         # for datasets requiring tokenization
@@ -220,6 +237,8 @@ def get_loaders(name, nsamples=128, seed=0, seqlen=2048, eval_mode=False, model_
 
         if name.lower() == "wikitext2":
             data = get_wikitext2(nsamples, seqlen, tokenizer, eval_mode=eval_mode)
+        elif name.lower() == "pajama":
+            data = get_red_pajama(nsamples, seqlen, tokenizer)
         elif name.lower() == "ptb":
             data = get_ptb(nsamples, seqlen, tokenizer, eval_mode=eval_mode)
         elif name.lower() == "ptb_new":
@@ -231,7 +250,7 @@ def get_loaders(name, nsamples=128, seed=0, seqlen=2048, eval_mode=False, model_
         else:
             raise ValueError(
                 f"Failed to load data from {name}.",
-                "Check dataset name or path or use one of [c4, wikitext2, ptb, pajama, refinedweb, none]",
+                "Check dataset name or path or use one of [c4, wikitext2, ptb, pajama, none]",
             )
 
     if hasattr(data, "input_ids"):